EosCloudによるSbD4Skin:皮膚感受性,刺激性,および急性皮膚毒性を予測するためのマルチビュー分子表現を統合
Nikoletta-Maria Koutroumpa1,2, Dimitra-Danai Varsou3, Panagiotis D Kolokathis3
1Entelos Institute, Nicosia 2102, Cyprus.
Computational and structural biotechnology journal
|August 20, 2025
まとめ
この研究は,様々な分子表現と機械学習を使用して皮膚の毒性を予測し,動物実験を減らすための計算フレームワークを提示しています. 開発されたモデルは,化学物質リスク評価のためのSbD4Skinプラットフォームで利用できます.
科学分野:
- コンピュータによる毒理学
- シリコ方式で
- 化学リスク評価
背景:
- 倫理的,財政的,科学的な課題は,化学的毒性の評価のために非動物実験方法を必要とします.
- 皮膚の敏感性,刺激/腐食,および急性皮膚毒性の予測は,ヒトの健康リスクの評価に不可欠です.
- 既存の方法はしばしば動物実験に依存し,代替のシリコンアプローチの必要性を高めています.
研究 の 目的:
- 主要な皮膚毒性のエンドポイントを予測するための計算的枠組みを開発し,検証する.
- 多様な分子表現 (MACCSキー,モルガン指紋,モルドレッド記述子) の毒性予測の有効性を評価する.
- モデルの透明性を高め,化学的毒性に関するメカニズム的な洞察を提供すること.
主な方法:
- 個々の分子表現を評価するために機械学習アルゴリズム (ランダムフォレスト,SVM,k-NN) を利用した.
- 様々な分子記述器を統合したマルチビュー完全接続ニューラルネットワーク (FCNN) を開発した.
- 特徴の重要性分析とモデルの解釈性のために,シャップリー・アディティブ・エクスプランテーション (SHAP) を採用した.
主要な成果:
- マルチビューFCNNモデルは,単一表示モデルよりも優れたまたは比較可能な予測性能を示した.
- 刺激/腐食に対するAUC値は0.91,敏感性に対するAUC値は0.88,急性皮膚毒性に対するAUC値は0.82に達した.
- 皮膚敏感剤の86%,刺激剤の89%,皮膚に有害な化合物の86%をテストセットで成功裏に特定しました.
結論:
- コンピューターのフレームワークは皮膚の毒性のエンドポイントを効果的に予測し,動物実験の有効な代替案を提供します.
- SbD4Skinのウェブプラットフォームは,規制の意思決定を支援するために,検証されたモデルへの無料アクセスを提供しています.
- FAIRifiedデータセットとモデルは,化学リスク評価の透明性,再利用性,および受容性を促進します.
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